Publications

@conference{Houiji2016229b,

title = {Fault detection performances analysis for stochastic systems based on adaptive threshold},

author = {Marwa Houiji and Rim Hamdaoui and Mohamed Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84974575297\&doi=10.1109%2fSSD.2016.7473701\&partnerID=40\&md5=0f9cdd28ba3852316e53997164690401},

doi = {10.1109/SSD.2016.7473701},

year  = {2016},

date = {2016-01-01},

journal = {13th International Multi-Conference on Systems, Signals and Devices, SSD 2016},

pages = {229 \textendash 234},

abstract = {This paper investigates the problem of fault detection for discrete linear systems subjected to unknown disturbances, actuator and sensor faults. A bank of Augmented Robust Three stage Kalman filters is adapted to estimate both the state and the fault as well as to generate the residuals. Besides, this paper presents the evaluation of the residuals with Bayes test of binary hypothesis test for fault detection to adaptive threshold compared with fixed threshold. This test allow the detection of low magnitude faults as fast as possible with a minimum risk of errors, the increase of detection probability and the reduction of false alarm probability. © 2016 IEEE.},

note = {Cited by: 2},

keywords = {Actuator and sensor faults, Adaptive thresholds, Binary hypothesis tests, Detection delays, Detection performance, Detection probabilities, Discrete linear systems, False alarm probability, Fault detection, Linear systems, Signal detection, Stochastic systems},

pubstate = {published},

tppubtype = {conference}

}

@conference{Houiji2016229,

title = {Fault detection performances analysis for stochastic systems based on adaptive threshold},

author = {Marwa Houiji and Rim Hamdaoui and Mohamed Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84974575297\&doi=10.1109%2fSSD.2016.7473701\&partnerID=40\&md5=0f9cdd28ba3852316e53997164690401},

doi = {10.1109/SSD.2016.7473701},

year  = {2016},

date = {2016-01-01},

journal = {13th International Multi-Conference on Systems, Signals and Devices, SSD 2016},

pages = {229 \textendash 234},

abstract = {This paper investigates the problem of fault detection for discrete linear systems subjected to unknown disturbances, actuator and sensor faults. A bank of Augmented Robust Three stage Kalman filters is adapted to estimate both the state and the fault as well as to generate the residuals. Besides, this paper presents the evaluation of the residuals with Bayes test of binary hypothesis test for fault detection to adaptive threshold compared with fixed threshold. This test allow the detection of low magnitude faults as fast as possible with a minimum risk of errors, the increase of detection probability and the reduction of false alarm probability. © 2016 IEEE.},

note = {Cited by: 2},

keywords = {Actuator and sensor faults, Adaptive thresholds, Binary hypothesis tests, Detection delays, Detection performance, Detection probabilities, Discrete linear systems, False alarm probability, Fault detection, Linear systems, Signal detection, Stochastic systems},

pubstate = {published},

tppubtype = {conference}

}

@conference{Aoun2011f,

title = {On the fractional systems' fault detection: A comparison between fractional and rational residual sensitivity},

author = {Mohamed Aoun and Asma Aribi and Slaheddine Najar and Mohamed Naceur Abdelkrim},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79957903547\&doi=10.1109%2fSSD.2011.5767424\&partnerID=40\&md5=2e22d74f672e2ae986db94c4dbcd72e6},

doi = {10.1109/SSD.2011.5767424},

year  = {2011},

date = {2011-01-01},

journal = {International Multi-Conference on Systems, Signals and Devices, SSD'11 - Summary Proceedings},

abstract = {This paper shows the interest of extending the dynamic parity space fault detection method for fractional systems. Accordingly, a comparison between fractional and rational residual generators using the later method is presented. An analysis of fractional and rational residuals' sensitivity shows the merits of the fractional residual generators. A numerical example illustrating the advantage of using fractional residual generators for fractional systems diagnosis is given. © 2011 IEEE.},

note = {Cited by: 8},

keywords = {Detection methods, Fault detection, fault diagnosis, Fractional derivatives, Fractional systems, Numerical example, Parity spaces, Residual generator, residual sensitivity, Signal detection},

pubstate = {published},

tppubtype = {conference}

}